Loading Data¶

In [1]:
import numpy as np
import pandas as pd
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.figure_factory as ff
In [2]:
df= pd.read_csv("athlete_events.csv")
region_df=pd.read_csv("noc_regions.csv")
In [3]:
df.tail()
Out[3]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal
271111 135569 Andrzej ya M 29.0 179.0 89.0 Poland-1 POL 1976 Winter 1976 Winter Innsbruck Luge Luge Mixed (Men)'s Doubles NaN
271112 135570 Piotr ya M 27.0 176.0 59.0 Poland POL 2014 Winter 2014 Winter Sochi Ski Jumping Ski Jumping Men's Large Hill, Individual NaN
271113 135570 Piotr ya M 27.0 176.0 59.0 Poland POL 2014 Winter 2014 Winter Sochi Ski Jumping Ski Jumping Men's Large Hill, Team NaN
271114 135571 Tomasz Ireneusz ya M 30.0 185.0 96.0 Poland POL 1998 Winter 1998 Winter Nagano Bobsleigh Bobsleigh Men's Four NaN
271115 135571 Tomasz Ireneusz ya M 34.0 185.0 96.0 Poland POL 2002 Winter 2002 Winter Salt Lake City Bobsleigh Bobsleigh Men's Four NaN
In [4]:
df.shape
Out[4]:
(271116, 15)

We will only work on summer olympics data

In [5]:
df=df[df['Season']=='Summer']
In [6]:
df.shape
Out[6]:
(222552, 15)
In [7]:
df.tail()
Out[7]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal
271106 135565 Fernando scar Zylberberg M 27.0 168.0 76.0 Argentina ARG 2004 Summer 2004 Summer Athina Hockey Hockey Men's Hockey NaN
271107 135566 James Francis "Jim" Zylker M 21.0 175.0 75.0 United States USA 1972 Summer 1972 Summer Munich Football Football Men's Football NaN
271108 135567 Aleksandr Viktorovich Zyuzin M 24.0 183.0 72.0 Russia RUS 2000 Summer 2000 Summer Sydney Rowing Rowing Men's Lightweight Coxless Fours NaN
271109 135567 Aleksandr Viktorovich Zyuzin M 28.0 183.0 72.0 Russia RUS 2004 Summer 2004 Summer Athina Rowing Rowing Men's Lightweight Coxless Fours NaN
271110 135568 Olga Igorevna Zyuzkova F 33.0 171.0 69.0 Belarus BLR 2016 Summer 2016 Summer Rio de Janeiro Basketball Basketball Women's Basketball NaN
In [8]:
region_df.tail()
Out[8]:
NOC region notes
225 YEM Yemen NaN
226 YMD Yemen South Yemen
227 YUG Serbia Yugoslavia
228 ZAM Zambia NaN
229 ZIM Zimbabwe NaN

Pre-Processing¶

In [9]:
df=df.merge(region_df,on='NOC', how='left')
df
Out[9]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal region notes
0 1 A Dijiang M 24.0 180.0 80.0 China CHN 1992 Summer 1992 Summer Barcelona Basketball Basketball Men's Basketball NaN China NaN
1 2 A Lamusi M 23.0 170.0 60.0 China CHN 2012 Summer 2012 Summer London Judo Judo Men's Extra-Lightweight NaN China NaN
2 3 Gunnar Nielsen Aaby M 24.0 NaN NaN Denmark DEN 1920 Summer 1920 Summer Antwerpen Football Football Men's Football NaN Denmark NaN
3 4 Edgar Lindenau Aabye M 34.0 NaN NaN Denmark/Sweden DEN 1900 Summer 1900 Summer Paris Tug-Of-War Tug-Of-War Men's Tug-Of-War Gold Denmark NaN
4 8 Cornelia "Cor" Aalten (-Strannood) F 18.0 168.0 NaN Netherlands NED 1932 Summer 1932 Summer Los Angeles Athletics Athletics Women's 100 metres NaN Netherlands NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
222547 135565 Fernando scar Zylberberg M 27.0 168.0 76.0 Argentina ARG 2004 Summer 2004 Summer Athina Hockey Hockey Men's Hockey NaN Argentina NaN
222548 135566 James Francis "Jim" Zylker M 21.0 175.0 75.0 United States USA 1972 Summer 1972 Summer Munich Football Football Men's Football NaN USA NaN
222549 135567 Aleksandr Viktorovich Zyuzin M 24.0 183.0 72.0 Russia RUS 2000 Summer 2000 Summer Sydney Rowing Rowing Men's Lightweight Coxless Fours NaN Russia NaN
222550 135567 Aleksandr Viktorovich Zyuzin M 28.0 183.0 72.0 Russia RUS 2004 Summer 2004 Summer Athina Rowing Rowing Men's Lightweight Coxless Fours NaN Russia NaN
222551 135568 Olga Igorevna Zyuzkova F 33.0 171.0 69.0 Belarus BLR 2016 Summer 2016 Summer Rio de Janeiro Basketball Basketball Women's Basketball NaN Belarus NaN

222552 rows × 17 columns

In [10]:
df['region'].unique()
Out[10]:
array(['China', 'Denmark', 'Netherlands', 'Finland', 'Norway', 'Romania',
       'Estonia', 'France', 'Morocco', 'Spain', 'Egypt', 'Iran',
       'Bulgaria', 'Italy', 'Chad', 'Azerbaijan', 'Sudan', 'Russia',
       'Argentina', 'Cuba', 'Belarus', 'Greece', 'Cameroon', 'Turkey',
       'Chile', 'Mexico', 'USA', 'Nicaragua', 'Hungary', 'Nigeria',
       'Algeria', 'Kuwait', 'Bahrain', 'Pakistan', 'Iraq', 'Syria',
       'Lebanon', 'Qatar', 'Malaysia', 'Germany', 'Canada', 'Ireland',
       'Australia', 'South Africa', 'Eritrea', 'Tanzania', 'Jordan',
       'Tunisia', 'Libya', 'Belgium', 'Djibouti', 'Palestine', 'Comoros',
       'Kazakhstan', 'Brunei', 'India', 'Saudi Arabia', 'Maldives',
       'Ethiopia', 'United Arab Emirates', 'Yemen', 'Indonesia',
       'Philippines', nan, 'Uzbekistan', 'Kyrgyzstan', 'Tajikistan',
       'Japan', 'Republic of Congo', 'Switzerland', 'Brazil', 'Monaco',
       'Israel', 'Uruguay', 'Sweden', 'Sri Lanka', 'Armenia',
       'Ivory Coast', 'Kenya', 'Benin', 'UK', 'Ghana', 'Somalia', 'Niger',
       'Mali', 'Afghanistan', 'Poland', 'Costa Rica', 'Panama', 'Georgia',
       'Slovenia', 'Guyana', 'New Zealand', 'Portugal', 'Paraguay',
       'Angola', 'Venezuela', 'Colombia', 'Bangladesh', 'Peru',
       'El Salvador', 'Puerto Rico', 'Uganda', 'Honduras', 'Ecuador',
       'Turkmenistan', 'Mauritius', 'Seychelles', 'Czech Republic',
       'Luxembourg', 'Mauritania', 'Saint Kitts', 'Trinidad',
       'Dominican Republic', 'Saint Vincent', 'Jamaica', 'Liberia',
       'Suriname', 'Nepal', 'Mongolia', 'Austria', 'Palau', 'Lithuania',
       'Togo', 'Namibia', 'Curacao', 'Ukraine', 'Iceland',
       'American Samoa', 'Samoa', 'Rwanda', 'Croatia', 'Dominica',
       'Haiti', 'Malta', 'Cyprus', 'Guinea', 'Belize', 'Thailand',
       'Bermuda', 'Serbia', 'Sierra Leone', 'Papua New Guinea',
       'Individual Olympic Athletes', 'Oman', 'Fiji', 'Vanuatu',
       'Moldova', 'Bahamas', 'Guatemala', 'Latvia',
       'Virgin Islands, British', 'Mozambique', 'Virgin Islands, US',
       'Central African Republic', 'Madagascar', 'Bosnia and Herzegovina',
       'Guam', 'Cayman Islands', 'Slovakia', 'Barbados', 'Guinea-Bissau',
       'Timor-Leste', 'Democratic Republic of the Congo', 'Gabon',
       'San Marino', 'Laos', 'Botswana', 'South Korea', 'Cambodia',
       'North Korea', 'Solomon Islands', 'Senegal', 'Cape Verde',
       'Equatorial Guinea', 'Boliva', 'Antigua', 'Andorra', 'Zimbabwe',
       'Grenada', 'Saint Lucia', 'Micronesia', 'Myanmar', 'Malawi',
       'Zambia', 'Taiwan', 'Sao Tome and Principe', 'Macedonia',
       'Liechtenstein', 'Montenegro', 'Gambia', 'Cook Islands', 'Albania',
       'Swaziland', 'Burkina Faso', 'Burundi', 'Aruba', 'Nauru',
       'Vietnam', 'Bhutan', 'Marshall Islands', 'Kiribati', 'Tonga',
       'Kosovo', 'South Sudan', 'Lesotho'], dtype=object)
In [11]:
df['region'].unique().shape
Out[11]:
(206,)
In [12]:
df.isnull().sum()
Out[12]:
ID             0
Name           0
Sex            0
Age         9189
Height     51857
Weight     53854
Team           0
NOC            0
Games          0
Year           0
Season         0
City           0
Sport          0
Event          0
Medal     188464
region       370
notes     218151
dtype: int64

Check and delete duplicate rows

In [13]:
df.duplicated().sum()
Out[13]:
1385
In [14]:
df.drop_duplicates(inplace=True)
In [15]:
df.duplicated().sum()
Out[15]:
0
In [16]:
df['Medal'].value_counts()
Out[16]:
Medal
Gold      11456
Bronze    11409
Silver    11212
Name: count, dtype: int64
In [17]:
pd.get_dummies(df['Medal'])
Out[17]:
Bronze Gold Silver
0 False False False
1 False False False
2 False False False
3 False True False
4 False False False
... ... ... ...
222547 False False False
222548 False False False
222549 False False False
222550 False False False
222551 False False False

221167 rows × 3 columns

In [18]:
df=pd.concat([df,pd.get_dummies(df['Medal'])],axis=1)
In [19]:
df.shape
Out[19]:
(221167, 20)
In [20]:
df.tail()
Out[20]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal region notes Bronze Gold Silver
222547 135565 Fernando scar Zylberberg M 27.0 168.0 76.0 Argentina ARG 2004 Summer 2004 Summer Athina Hockey Hockey Men's Hockey NaN Argentina NaN False False False
222548 135566 James Francis "Jim" Zylker M 21.0 175.0 75.0 United States USA 1972 Summer 1972 Summer Munich Football Football Men's Football NaN USA NaN False False False
222549 135567 Aleksandr Viktorovich Zyuzin M 24.0 183.0 72.0 Russia RUS 2000 Summer 2000 Summer Sydney Rowing Rowing Men's Lightweight Coxless Fours NaN Russia NaN False False False
222550 135567 Aleksandr Viktorovich Zyuzin M 28.0 183.0 72.0 Russia RUS 2004 Summer 2004 Summer Athina Rowing Rowing Men's Lightweight Coxless Fours NaN Russia NaN False False False
222551 135568 Olga Igorevna Zyuzkova F 33.0 171.0 69.0 Belarus BLR 2016 Summer 2016 Summer Rio de Janeiro Basketball Basketball Women's Basketball NaN Belarus NaN False False False

filter medals with NOC(Region)

In [21]:
df.groupby('NOC').sum()[['Gold','Silver','Bronze']].sort_values(['Gold','Silver','Bronze'],ascending=False).reset_index()
Out[21]:
NOC Gold Silver Bronze
0 USA 2472 1333 1197
1 URS 832 635 596
2 GBR 635 729 620
3 GER 592 538 649
4 ITA 518 474 454
... ... ... ... ...
225 VIN 0 0 0
226 VNM 0 0 0
227 YAR 0 0 0
228 YEM 0 0 0
229 YMD 0 0 0

230 rows × 4 columns

In [22]:
df[(df['NOC']=='IND') & (df['Medal']=='Gold')]
Out[22]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal region notes Bronze Gold Silver
4186 2699 Shaukat Ali M 30.0 NaN NaN India IND 1928 Summer 1928 Summer Amsterdam Hockey Hockey Men's Hockey Gold India NaN False True False
4190 2703 Syed Mushtaq Ali M 22.0 165.0 61.0 India IND 1964 Summer 1964 Summer Tokyo Hockey Hockey Men's Hockey Gold India NaN False True False
4460 2864 Richard James Allen M 25.0 172.0 NaN India IND 1928 Summer 1928 Summer Amsterdam Hockey Hockey Men's Hockey Gold India NaN False True False
4461 2864 Richard James Allen M 30.0 172.0 NaN India IND 1932 Summer 1932 Summer Los Angeles Hockey Hockey Men's Hockey Gold India NaN False True False
4462 2864 Richard James Allen M 34.0 172.0 NaN India IND 1936 Summer 1936 Summer Berlin Hockey Hockey Men's Hockey Gold India NaN False True False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
191759 117232 Dung Dung Sylvanus M 31.0 160.0 62.0 India IND 1980 Summer 1980 Summer Moskva Hockey Hockey Men's Hockey Gold India NaN False True False
193964 118553 Carlyle Carrol Tapsell M 23.0 182.0 NaN India IND 1932 Summer 1932 Summer Los Angeles Hockey Hockey Men's Hockey Gold India NaN False True False
193965 118553 Carlyle Carrol Tapsell M 27.0 182.0 NaN India IND 1936 Summer 1936 Summer Berlin Hockey Hockey Men's Hockey Gold India NaN False True False
205949 125560 Max "Maxie" Vaz M NaN NaN NaN India IND 1948 Summer 1948 Summer London Hockey Hockey Men's Hockey Gold India NaN False True False
219336 133554 Sayed Muhammad Yusuf M NaN NaN NaN India IND 1928 Summer 1928 Summer Amsterdam Hockey Hockey Men's Hockey Gold India NaN False True False

131 rows × 20 columns

Medal Tally¶

In [23]:
medal_tally=df.drop_duplicates(['Team','NOC','Games','Year','City','Sport','Event','Medal'])
In [24]:
medal_tally.shape
Out[24]:
(105636, 20)
In [25]:
medal_tally.head()
Out[25]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal region notes Bronze Gold Silver
0 1 A Dijiang M 24.0 180.0 80.0 China CHN 1992 Summer 1992 Summer Barcelona Basketball Basketball Men's Basketball NaN China NaN False False False
1 2 A Lamusi M 23.0 170.0 60.0 China CHN 2012 Summer 2012 Summer London Judo Judo Men's Extra-Lightweight NaN China NaN False False False
2 3 Gunnar Nielsen Aaby M 24.0 NaN NaN Denmark DEN 1920 Summer 1920 Summer Antwerpen Football Football Men's Football NaN Denmark NaN False False False
3 4 Edgar Lindenau Aabye M 34.0 NaN NaN Denmark/Sweden DEN 1900 Summer 1900 Summer Paris Tug-Of-War Tug-Of-War Men's Tug-Of-War Gold Denmark NaN False True False
4 8 Cornelia "Cor" Aalten (-Strannood) F 18.0 168.0 NaN Netherlands NED 1932 Summer 1932 Summer Los Angeles Athletics Athletics Women's 100 metres NaN Netherlands NaN False False False
In [26]:
medal_tally= medal_tally.groupby('NOC').sum()[['Gold','Silver','Bronze']].sort_values(['Gold','Silver','Bronze'],ascending=False).reset_index()
In [27]:
medal_tally
Out[27]:
NOC Gold Silver Bronze
0 USA 1035 802 708
1 URS 394 317 294
2 GBR 278 317 300
3 GER 235 261 283
4 FRA 234 256 287
... ... ... ... ...
225 VIN 0 0 0
226 VNM 0 0 0
227 YAR 0 0 0
228 YEM 0 0 0
229 YMD 0 0 0

230 rows × 4 columns

In [28]:
medal_tally[medal_tally['NOC']=='IND']
Out[28]:
NOC Gold Silver Bronze
53 IND 9 7 12
In [29]:
years=df['Year'].unique().tolist()
years.sort()
years.insert(0,'Overall')
years
Out[29]:
['Overall',
 1896,
 1900,
 1904,
 1906,
 1908,
 1912,
 1920,
 1924,
 1928,
 1932,
 1936,
 1948,
 1952,
 1956,
 1960,
 1964,
 1968,
 1972,
 1976,
 1980,
 1984,
 1988,
 1992,
 1996,
 2000,
 2004,
 2008,
 2012,
 2016]
In [30]:
country=np.unique(df['region'].dropna().values).tolist()
country.sort()
country.insert(0,'Overall')
country
Out[30]:
['Overall',
 'Afghanistan',
 'Albania',
 'Algeria',
 'American Samoa',
 'Andorra',
 'Angola',
 'Antigua',
 'Argentina',
 'Armenia',
 'Aruba',
 'Australia',
 'Austria',
 'Azerbaijan',
 'Bahamas',
 'Bahrain',
 'Bangladesh',
 'Barbados',
 'Belarus',
 'Belgium',
 'Belize',
 'Benin',
 'Bermuda',
 'Bhutan',
 'Boliva',
 'Bosnia and Herzegovina',
 'Botswana',
 'Brazil',
 'Brunei',
 'Bulgaria',
 'Burkina Faso',
 'Burundi',
 'Cambodia',
 'Cameroon',
 'Canada',
 'Cape Verde',
 'Cayman Islands',
 'Central African Republic',
 'Chad',
 'Chile',
 'China',
 'Colombia',
 'Comoros',
 'Cook Islands',
 'Costa Rica',
 'Croatia',
 'Cuba',
 'Curacao',
 'Cyprus',
 'Czech Republic',
 'Democratic Republic of the Congo',
 'Denmark',
 'Djibouti',
 'Dominica',
 'Dominican Republic',
 'Ecuador',
 'Egypt',
 'El Salvador',
 'Equatorial Guinea',
 'Eritrea',
 'Estonia',
 'Ethiopia',
 'Fiji',
 'Finland',
 'France',
 'Gabon',
 'Gambia',
 'Georgia',
 'Germany',
 'Ghana',
 'Greece',
 'Grenada',
 'Guam',
 'Guatemala',
 'Guinea',
 'Guinea-Bissau',
 'Guyana',
 'Haiti',
 'Honduras',
 'Hungary',
 'Iceland',
 'India',
 'Individual Olympic Athletes',
 'Indonesia',
 'Iran',
 'Iraq',
 'Ireland',
 'Israel',
 'Italy',
 'Ivory Coast',
 'Jamaica',
 'Japan',
 'Jordan',
 'Kazakhstan',
 'Kenya',
 'Kiribati',
 'Kosovo',
 'Kuwait',
 'Kyrgyzstan',
 'Laos',
 'Latvia',
 'Lebanon',
 'Lesotho',
 'Liberia',
 'Libya',
 'Liechtenstein',
 'Lithuania',
 'Luxembourg',
 'Macedonia',
 'Madagascar',
 'Malawi',
 'Malaysia',
 'Maldives',
 'Mali',
 'Malta',
 'Marshall Islands',
 'Mauritania',
 'Mauritius',
 'Mexico',
 'Micronesia',
 'Moldova',
 'Monaco',
 'Mongolia',
 'Montenegro',
 'Morocco',
 'Mozambique',
 'Myanmar',
 'Namibia',
 'Nauru',
 'Nepal',
 'Netherlands',
 'New Zealand',
 'Nicaragua',
 'Niger',
 'Nigeria',
 'North Korea',
 'Norway',
 'Oman',
 'Pakistan',
 'Palau',
 'Palestine',
 'Panama',
 'Papua New Guinea',
 'Paraguay',
 'Peru',
 'Philippines',
 'Poland',
 'Portugal',
 'Puerto Rico',
 'Qatar',
 'Republic of Congo',
 'Romania',
 'Russia',
 'Rwanda',
 'Saint Kitts',
 'Saint Lucia',
 'Saint Vincent',
 'Samoa',
 'San Marino',
 'Sao Tome and Principe',
 'Saudi Arabia',
 'Senegal',
 'Serbia',
 'Seychelles',
 'Sierra Leone',
 'Slovakia',
 'Slovenia',
 'Solomon Islands',
 'Somalia',
 'South Africa',
 'South Korea',
 'South Sudan',
 'Spain',
 'Sri Lanka',
 'Sudan',
 'Suriname',
 'Swaziland',
 'Sweden',
 'Switzerland',
 'Syria',
 'Taiwan',
 'Tajikistan',
 'Tanzania',
 'Thailand',
 'Timor-Leste',
 'Togo',
 'Tonga',
 'Trinidad',
 'Tunisia',
 'Turkey',
 'Turkmenistan',
 'UK',
 'USA',
 'Uganda',
 'Ukraine',
 'United Arab Emirates',
 'Uruguay',
 'Uzbekistan',
 'Vanuatu',
 'Venezuela',
 'Vietnam',
 'Virgin Islands, British',
 'Virgin Islands, US',
 'Yemen',
 'Zambia',
 'Zimbabwe']
In [31]:
def fetch_medal_tally(df, year, country):
    medal_df = df.drop_duplicates(subset=['Team', 'NOC', 'Games', 'Year', 'City', 'Sport', 'Event', 'Medal'])
    flag = 0
    if year == 'Overall' and country == 'Overall':
        temp_df = medal_df
    if year == 'Overall' and country != 'Overall':
        flag = 1
        temp_df = medal_df[medal_df['region'] == country]
    if year != 'Overall' and country == 'Overall':
        temp_df = medal_df[medal_df['Year'] == int(year)]
    if year != 'Overall' and country != 'Overall':
        temp_df = medal_df[(medal_df['Year'] == year) & (medal_df['region'] == country)]

    if flag == 1:
        x = temp_df.groupby('Year').sum()[['Gold', 'Silver', 'Bronze']].sort_values('Year').reset_index()
    else:
        x = temp_df.groupby('region').sum()[['Gold', 'Silver', 'Bronze']].sort_values('Gold',ascending=False).reset_index()

    x['total'] = x['Gold'] + x['Silver'] + x['Bronze']

    x['Gold'] = x['Gold'].astype('int')
    x['Silver'] = x['Silver'].astype('int')
    x['Bronze'] = x['Bronze'].astype('int')
    x['total'] = x['total'].astype('int')

    return x
In [32]:
fetch_medal_tally(df,2016,'India')
Out[32]:
region Gold Silver Bronze total
0 India 0 1 1 2

Overall Analysis¶

  • Number of Editions
  • Number of Cities
  • Number of Events/Sports
  • Number of Athelets
  • Participating Nations
    • </ui>

Top Statistics¶

In [33]:
df['Year'].unique().shape[0]-1             #as 2016 olympic was dismissed
Out[33]:
28
In [34]:
df['City'].unique().shape[0]
Out[34]:
23
In [35]:
df['Event'].unique().shape[0]
Out[35]:
651
In [36]:
df['Name'].unique().shape[0]
Out[36]:
116122
In [37]:
df['region'].unique().shape[0]
Out[37]:
206

Ploting number of countries participating every year¶

In [38]:
nations_over_time=df.drop_duplicates(['Year','region'])['Year'].value_counts().reset_index()
nations_over_time.tail(3)
Out[38]:
Year count
26 1906 20
27 1904 14
28 1896 12
In [39]:
fig=px.line(nations_over_time, x='Year', y='count')
fig.update_layout(width=1000,height=700,title='Participating Nations over the years')
fig.show()
In [40]:
#nunique() to count the number of unique years within each group.
region_participation = df.groupby('region')['Year'].nunique().reset_index()
region_participation

#try to make this function for all graphs
Out[40]:
region Year
0 Afghanistan 14
1 Albania 8
2 Algeria 13
3 American Samoa 8
4 Andorra 11
... ... ...
200 Virgin Islands, British 9
201 Virgin Islands, US 12
202 Yemen 9
203 Zambia 13
204 Zimbabwe 13

205 rows × 2 columns

In [41]:
fig = px.choropleth(region_participation,
                   locations='region',
                   color = 'Year',
                   locationmode='country names')
fig.update_layout(
    width=1000,
    height=500,
    title='Participating Nations over the years'
)

fig.show()
In [42]:
events_over_time=df.drop_duplicates(['Year','Event'])['Year'].value_counts().reset_index()
events_over_time
Out[42]:
Year count
0 2016 306
1 2008 302
2 2012 302
3 2004 301
4 2000 300
5 1996 271
6 1992 257
7 1988 237
8 1984 221
9 1980 203
10 1976 198
11 1972 193
12 1968 172
13 1964 163
14 1920 158
15 1948 153
16 1956 151
17 1960 150
18 1936 150
19 1952 149
20 1924 131
21 1932 131
22 1928 122
23 1908 109
24 1912 107
25 1904 95
26 1900 90
27 1906 74
28 1896 43
In [43]:
fig=px.line(events_over_time, x='Year', y='count')
fig.update_layout(width=1000,height=700, title="Events over the years")
fig.show()
In [44]:
athelets_over_time=df.drop_duplicates(['Year','Name'])['Year'].value_counts().reset_index()
fig=px.line(athelets_over_time, x='Year', y='count')
fig.update_layout(width=1000,height=700, title="Athelets over the years")
fig.show()
In [45]:
x=df.drop_duplicates(['Year','Sport','Event'])
x=x.pivot_table(index='Sport',columns='Year',values='Event',aggfunc='count').fillna(0).astype(int)
x
Out[45]:
Year 1896 1900 1904 1906 1908 1912 1920 1924 1928 1932 ... 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016
Sport
Aeronautics 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Alpinism 0 0 0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 0 0
Archery 0 8 6 0 3 0 10 0 0 0 ... 2 2 4 4 4 4 4 4 4 4
Art Competitions 0 0 0 0 0 5 5 5 13 13 ... 0 0 0 0 0 0 0 0 0 0
Athletics 12 23 24 21 26 30 29 27 27 29 ... 38 41 42 43 44 46 46 47 47 47
Badminton 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 4 5 5 5 5 5 5
Baseball 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 1 1 1 1 1 0 0
Basketball 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2
Basque Pelota 0 1 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Beach Volleyball 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 2 2 2 2 2 2
Boxing 0 0 7 0 5 0 8 8 8 8 ... 11 12 12 12 12 12 11 11 13 13
Canoeing 0 0 0 0 0 0 0 0 0 0 ... 11 12 12 16 16 16 16 16 16 16
Cricket 0 1 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Croquet 0 3 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Cycling 6 3 7 6 6 2 6 6 6 6 ... 6 8 9 10 14 18 18 18 18 18
Diving 0 0 1 1 2 4 5 5 4 4 ... 4 4 4 4 4 8 8 8 8 8
Equestrianism 0 5 0 0 0 5 7 5 6 6 ... 6 6 6 6 6 6 6 6 6 6
Fencing 3 7 5 8 4 5 6 7 7 7 ... 8 8 8 8 10 10 10 10 10 10
Figure Skating 0 0 0 0 4 0 3 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Football 0 1 1 1 1 1 1 1 1 0 ... 1 1 1 1 2 2 2 2 2 2
Golf 0 2 2 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 2
Gymnastics 8 1 12 4 2 4 4 9 8 11 ... 14 14 14 14 14 14 14 14 14 14
Handball 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2
Hockey 0 0 0 0 1 0 1 0 1 1 ... 2 2 2 2 2 2 2 2 2 2
Ice Hockey 0 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Jeu De Paume 0 0 0 0 1 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Judo 0 0 0 0 0 0 0 0 0 0 ... 8 8 7 14 14 14 14 14 14 14
Lacrosse 0 0 1 0 1 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Modern Pentathlon 0 0 0 0 0 1 1 1 1 1 ... 2 2 2 2 1 2 2 2 2 2
Motorboating 0 0 0 0 3 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Polo 0 1 0 0 1 0 1 1 0 0 ... 0 0 0 0 0 0 0 0 0 0
Racquets 0 0 0 0 2 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Rhythmic Gymnastics 0 0 0 0 0 0 0 0 0 0 ... 0 1 1 1 2 2 2 2 2 2
Roque 0 0 1 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Rowing 0 4 5 6 4 4 5 7 7 7 ... 14 14 14 14 14 14 14 14 14 14
Rugby 0 1 0 0 1 0 1 1 0 0 ... 0 0 0 0 0 0 0 0 0 0
Rugby Sevens 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 2
Sailing 0 8 0 0 4 4 10 3 3 4 ... 6 7 8 10 10 11 11 11 10 10
Shooting 5 8 0 12 15 18 22 10 0 2 ... 7 11 13 13 15 17 17 15 15 15
Softball 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 1 1 1 1 0 0
Swimming 4 7 10 4 6 9 10 11 11 11 ... 26 29 31 31 32 32 32 34 34 34
Synchronized Swimming 0 0 0 0 0 0 0 0 0 0 ... 0 2 2 2 1 2 2 2 2 2
Table Tennis 0 0 0 0 0 0 0 0 0 0 ... 0 0 4 4 4 4 4 4 4 4
Taekwondo 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 8 8 8 8 8
Tennis 2 4 2 4 6 8 5 5 0 0 ... 0 0 4 4 4 4 4 4 5 5
Trampolining 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 2 2 2 2 2
Triathlon 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 2 2 2 2 2
Tug-Of-War 0 1 1 1 1 1 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Volleyball 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2
Water Polo 0 1 1 0 1 1 1 1 1 1 ... 1 1 1 1 1 2 2 2 2 2
Weightlifting 2 0 2 2 0 0 5 5 5 5 ... 10 10 10 10 10 15 15 15 15 15
Wrestling 1 0 7 4 9 5 10 13 13 14 ... 20 20 20 20 20 16 18 18 18 18

52 rows × 29 columns

In [46]:
plt.subplots(figsize=(20,20))
sns.heatmap(x, annot=True).set(title ="Number of events over Year")
Out[46]:
[Text(0.5, 1.0, 'Number of events over Year')]
In [47]:
sport=np.unique(df['Sport'].dropna().values).tolist()
sport.sort()
sport.insert(0,'Overall')
sport
Out[47]:
['Overall',
 'Aeronautics',
 'Alpinism',
 'Archery',
 'Art Competitions',
 'Athletics',
 'Badminton',
 'Baseball',
 'Basketball',
 'Basque Pelota',
 'Beach Volleyball',
 'Boxing',
 'Canoeing',
 'Cricket',
 'Croquet',
 'Cycling',
 'Diving',
 'Equestrianism',
 'Fencing',
 'Figure Skating',
 'Football',
 'Golf',
 'Gymnastics',
 'Handball',
 'Hockey',
 'Ice Hockey',
 'Jeu De Paume',
 'Judo',
 'Lacrosse',
 'Modern Pentathlon',
 'Motorboating',
 'Polo',
 'Racquets',
 'Rhythmic Gymnastics',
 'Roque',
 'Rowing',
 'Rugby',
 'Rugby Sevens',
 'Sailing',
 'Shooting',
 'Softball',
 'Swimming',
 'Synchronized Swimming',
 'Table Tennis',
 'Taekwondo',
 'Tennis',
 'Trampolining',
 'Triathlon',
 'Tug-Of-War',
 'Volleyball',
 'Water Polo',
 'Weightlifting',
 'Wrestling']
In [48]:
def most_successful_sportwise(df, sport):
    temp_df = df.dropna(subset=['Medal'])
    if sport != "Overall":
        temp_df= temp_df[temp_df['Sport']==sport]
    x= temp_df['Name'].value_counts().reset_index().head(15).merge(df,left_on='Name',right_on='Name',how='left')[['Name','count','Sport','region']].drop_duplicates('Name')
    x.rename(columns={'count':'No. of Medals'},inplace=True)
    return x
In [49]:
most_successful_sportwise(df,'Overall')
Out[49]:
Name No. of Medals Sport region
0 Michael Fred Phelps, II 28 Swimming USA
30 Larysa Semenivna Latynina (Diriy-) 18 Gymnastics Russia
49 Nikolay Yefimovich Andrianov 15 Gymnastics Russia
73 Borys Anfiyanovych Shakhlin 13 Gymnastics Russia
97 Takashi Ono 13 Gymnastics Japan
130 Edoardo Mangiarotti 13 Fencing Italy
144 Dara Grace Torres (-Hoffman, -Minas) 12 Swimming USA
157 Aleksey Yuryevich Nemov 12 Gymnastics Russia
178 Jennifer Elisabeth "Jenny" Thompson (-Cumpelik) 12 Swimming USA
195 Birgit Fischer-Schmidt 12 Canoeing Germany
208 Ryan Steven Lochte 12 Swimming USA
222 Paavo Johannes Nurmi 12 Athletics Finland
234 Sawao Kato 12 Gymnastics Japan
258 Natalie Anne Coughlin (-Hall) 12 Swimming USA
270 Matthew Nicholas "Matt" Biondi 11 Swimming USA
In [50]:
most_successful_sportwise(df,'Gymnastics')
Out[50]:
Name No. of Medals Sport region
0 Larysa Semenivna Latynina (Diriy-) 18 Gymnastics Russia
19 Nikolay Yefimovich Andrianov 15 Gymnastics Russia
43 Borys Anfiyanovych Shakhlin 13 Gymnastics Russia
67 Takashi Ono 13 Gymnastics Japan
100 Sawao Kato 12 Gymnastics Japan
124 Aleksey Yuryevich Nemov 12 Gymnastics Russia
145 Viktor Ivanovych Chukarin 11 Gymnastics Russia
161 Vra slavsk (-Odloilov) 11 Gymnastics Czech Republic
179 gnes Keleti-Srkny (Klein) 10 Gymnastics Hungary
193 Vitaly Venediktovich Shcherbo 10 Gymnastics Russia
209 Aleksandr Nikolayevich Dityatin 10 Gymnastics Russia
225 Polina Hryhorivna Astakhova 10 Gymnastics Russia
244 Akinori Nakayama 10 Gymnastics Japan
260 Nadia Elena Comneci (-Conner) 9 Gymnastics Romania
272 Yury Yevlampiyevich Titov 9 Gymnastics Russia

Country Wise Analysis¶

  • Country wise Medal tally per year(line graph)
  • What countries are good at(Heatmap)
  • Most sucessfull atheletes(top 10)

Country wise Medal tally per year(line graph¶

In [51]:
temp_df=df.dropna(subset=['Medal'])
temp_df.drop_duplicates(subset=['Team','NOC','Games','Year','City','Sport','Event','Medal'], inplace=True)
In [52]:
new_df=temp_df[temp_df['region']=='India']
final_df=new_df.groupby('Year').count()['Medal'].reset_index()
In [53]:
fig= px.line(final_df, x='Year',y='Medal')
fig.update_layout(width=1000,height=700, title="Country wise Medal tally per year")
fig.show()

What countries are good at(Heatmap)¶

In [54]:
new_df=temp_df[temp_df['region']=="USA"]
final_df=new_df.pivot_table(index='Sport',columns='Year',values='Medal',aggfunc='count').fillna(0)
In [55]:
plt.subplots(figsize=(20,20))
sns.heatmap(final_df, annot=True).set(title ="Sports County Good at")
Out[55]:
[Text(0.5, 1.0, 'Sports County Good at')]

Most sucessfull atheletes(top 10)¶

In [56]:
def most_successful_countrywise(df, country):
    temp_df = df.dropna(subset=['Medal'])
    if sport != "Overall":
        temp_df= temp_df[temp_df['region']==country]
    x= temp_df['Name'].value_counts().reset_index().head(15).merge(df,left_on='Name',right_on='Name',how='left')[['Name','count','Sport']].drop_duplicates('Name')
    x.rename(columns={'count':'No. of Medals'},inplace=True)
    return x
In [57]:
most_successful_countrywise(df, 'USA')
Out[57]:
Name No. of Medals Sport
0 Michael Fred Phelps, II 28 Swimming
30 Ryan Steven Lochte 12 Swimming
44 Jennifer Elisabeth "Jenny" Thompson (-Cumpelik) 12 Swimming
61 Natalie Anne Coughlin (-Hall) 12 Swimming
73 Dara Grace Torres (-Hoffman, -Minas) 12 Swimming
86 Matthew Nicholas "Matt" Biondi 11 Swimming
98 Carl Townsend Osburn 11 Shooting
114 Mark Andrew Spitz 11 Swimming
126 Raymond Clarence "Ray" Ewry 10 Athletics
136 Frederick Carlton "Carl" Lewis 10 Athletics
146 Gary Wayne Hall, Jr. 10 Swimming
156 Allyson Michelle Felix 9 Athletics
166 Martin Joseph Sheridan 9 Athletics
181 Shirley Frances Babashoff 9 Swimming
192 Nathan Ghar-Jun Adrian 8 Swimming

Ahelete Analysis¶

In [58]:
athelete_df=df.drop_duplicates(subset=['Name','region'])
x=athelete_df['Age'].dropna()
x2=athelete_df[athelete_df['Medal']=='Gold']['Age'].dropna()
x3=athelete_df[athelete_df['Medal']=='Silver']['Age'].dropna()
x4 =athelete_df[athelete_df['Medal']=='Bronze']['Age'].dropna()
In [59]:
fig=ff.create_distplot([x,x2,x3,x4],['Overall','Gold Medalist','Silver Medalist','Bronze Medalist'],show_hist=False,show_rug=False)
fig.update_layout(
    width=800,  # Width of the figure in pixels
    height=400,  # Height of the figure in pixels
    title='Age Distribution'
)
fig.show()
In [60]:
famous_sports = ['Basketball', 'Judo', 'Football', 'Tug-Of-War', 'Athletics',
                     'Swimming', 'Badminton', 'Sailing', 'Gymnastics',
                     'Art Competitions', 'Handball', 'Weightlifting', 'Wrestling',
                     'Water Polo', 'Hockey', 'Rowing', 'Fencing',
                     'Shooting', 'Boxing', 'Taekwondo', 'Cycling', 'Diving', 'Canoeing',
                     'Tennis', 'Golf', 'Softball', 'Archery',
                     'Volleyball', 'Synchronized Swimming', 'Table Tennis', 'Baseball',
                     'Rhythmic Gymnastics', 'Rugby Sevens',
                     'Beach Volleyball', 'Triathlon', 'Rugby', 'Polo', 'Ice Hockey']
In [61]:
x=[]
name=[]
for sport in famous_sports:
    temp_df=athelete_df[athelete_df['Sport']==sport]
    x.append(temp_df[temp_df['Medal']=='Gold']['Age'].dropna())
    name.append(sport)
In [62]:
fig=ff.create_distplot(x,name,show_hist=False,show_rug=False)
fig.update_layout(
    width=800,  
    height=400, 
    title='Age Distribution wrt to gold medalist in sports'
)
fig.show()
In [63]:
df['Medal'].fillna('No Medal', inplace=True)
In [64]:
temp_df=athelete_df[athelete_df['Sport']=='Swimming']
plt.figure(figsize=(15, 10))
sns.scatterplot(temp_df,x='Weight',y='Height',hue='Medal',style='Sex',s=100)        #s= dot size
Out[64]:
<Axes: xlabel='Weight', ylabel='Height'>

men vs women participation¶

In [65]:
athlete_df = df.drop_duplicates(subset=['Name', 'region'])

men = athlete_df[athlete_df['Sex'] == 'M'].groupby('Year').count()['Name'].reset_index()
women = athlete_df[athlete_df['Sex'] == 'F'].groupby('Year').count()['Name'].reset_index()
In [66]:
final = men.merge(women, on='Year', how='left')
final.rename(columns={'Name_x': 'Male', 'Name_y': 'Female'}, inplace=True)
final.fillna(0, inplace=True)
In [67]:
fig = px.line(final, x="Year", y=["Male", "Female"])
fig.update_layout(autosize=False, width=1000, height=600)
In [ ]: